20-Year Trends in Metabolic Syndrome Among Korean Adults From 2001 to 2020
Bibliographic record
Abstract
Background: The number of people with metabolic syndrome (MetS) is increasing worldwide, and many socioeconomic and environmental factors contribute to this. Objectives: The authors investigated tangible trends in the prevalence of MetS using the 2001 to 2020 versions of the Korea National Health and Nutrition Examination Survey (KNHANES). Methods: In these surveys, stratified multistage sampling designs were used to approximate the entire population. Blood pressure, waist circumference, and lifestyle variables were examined in a standardized fashion. Metabolic biomarkers were measured in a central laboratory operated by the Korean government. Results: The age-adjusted prevalence of MetS increased significantly from 27.1% in 2001 to 33.2% in 2020. It was more prevalent in men (25.8%→40.0%) but did not change in women (28.2%→26.2%). Among the 5 MetS components, the proportions of high glucose level and large waist circumference increased substantially by 17.9% and 12.2% over 20 years, while high-density lipoprotein cholesterol levels increased significantly, resulting in a decrease in low high-density lipoprotein cholesterol by 20.4%. Caloric intake derived from carbohydrates decreased from 68.1% to 61.3%, while fat consumption increased from 16.7% to 23.0%. Notably, sugar-sweetened beverage consumption showed an almost 4-fold increase from 2007 to 2020, while physical activity levels decreased by 12.2% from 2014 to 2020. Conclusions: Glycemic dysregulation and abdominal obesity were key features contributing to the increased prevalence of MetS observed in Korean men during the past 20 years. Rapid economic and socioenvironmental changes in this period may be involved in this phenomenon. Understanding these changes in MetS could be valuable for other countries undergoing such socioeconomic transformation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".